Investigation of multi-performance optimization of banana/bark cloth reinforced epoxy composites using grey relational analysis for automotive interior applications

This study presents a novel multi-performance optimization of underexplored hybrid banana- and bark-cloth fiber-reinforced epoxy composites for automotive interior applications, using Grey Relational Analysis (GRA). Unlike previous studies that mainly examine banana fiber alone or other conventional natural fiber hybrids, the present work investigates the synergistic combination of banana fiber and bark cloth in an epoxy matrix with 5 wt% aluminium trihydrate (ATH) to simultaneously improve mechanical performance, flame resistance, and moisture-related durability. The composites were produced through compression moulding with varying fiber ratios, and their tensile, flexural, impact, flammability, and water absorption behaviours were systematically evaluated. Among the tested configurations, Sample C7 (40% banana, 5% bark cloth) exhibited the best overall performance, with tensile strength of 28.29 MPa, flexural strength of 37.86 MPa, specific impact energy of 106.92 J/g, and a burning rate of 1.053 mm/s. GRA was used to rank the composite formulations based on multiple response criteria, identifying C7 as the optimal configuration. SEM, EDAX, and FTIR analyses further confirmed improved fibre-matrix interaction, additive retention, and chemical compatibility. The findings demonstrate the potential of banana/bark cloth hybrid composites as sustainable multifunctional materials for automotive interiors.

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Publication Details

Journal
Scientific Reports
Published
2026-03-23
DOI
https://doi.org/10.1038/s41598-026-45783-9
Primary Topic
Natural Fiber Reinforced Composites
Type
article
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article

Investigation of multi-performance optimization of banana/bark cloth reinforced epoxy composites using grey relational analysis for automotive interior applications

Onep Samuel George, Milon Selvam Dennison, Alex Turyahabwe
Scientific Reports
Natural Fiber Reinforced Composites
article

Investigation of multi-performance optimization of banana/bark cloth reinforced epoxy composites using grey relational analysis for automotive interior applications

Onep Samuel George, Milon Selvam Dennison, Alex Turyahabwe
article en

Abstract

This study presents a novel multi-performance optimization of underexplored hybrid banana- and bark-cloth fiber-reinforced epoxy composites for automotive interior applications, using Grey Relational Analysis (GRA). Unlike previous studies that mainly examine banana fiber alone or other conventional natural fiber hybrids, the present work investigates the synergistic combination of banana fiber and bark cloth in an epoxy matrix with 5 wt% aluminium trihydrate (ATH) to simultaneously improve mechanical performance, flame resistance, and moisture-related durability. The composites were produced through compression moulding with varying fiber ratios, and their tensile, flexural, impact, flammability, and water absorption behaviours were systematically evaluated. Among the tested configurations, Sample C7 (40% banana, 5% bark cloth) exhibited the best overall performance, with tensile strength of 28.29 MPa, flexural strength of 37.86 MPa, specific impact energy of 106.92 J/g, and a burning rate of 1.053 mm/s. GRA was used to rank the composite formulations based on multiple response criteria, identifying C7 as the optimal configuration. SEM, EDAX, and FTIR analyses further confirmed improved fibre-matrix interaction, additive retention, and chemical compatibility. The findings demonstrate the potential of banana/bark cloth hybrid composites as sustainable multifunctional materials for automotive interiors.

Scientific Reports
Industry, innovation and infrastructure
Openalex Percentile: Top 12%
Natural Fiber Reinforced Composites
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Investigation of multi-performance optimization of banana/bark cloth reinforced epoxy composites using grey relational analysis for automotive interior applications — Onep Samuel George, Milon Selvam Dennison, et al. · Scientific Reports (2026) | TGRS Research Map | TGRS